NURS 701 - Statistical Analysis – Assignment 9
(Due by November 29th, 2017) – 27points

1 –Factor Analysis of Cereal Data (sorry not medical) – Rate Cereals.JMP

As part of a study of consumer consideration of ready-to-eat cereals sponsored by Kellogg Australia, Roberts and Lattin (1991) surveyed consumers regarding their perceptions of their favorite brands of cereals. Each respondent was asked to evaluate three preferred brands on each of 25 different attributes. Respondents used a five-point scale to indicate the extent to which each brand possessed the given attribute.

The 25 attributes were:

FillingNaturalFiberSweet

EasySaltSatisfyingEnergy

FunKidsSoggyEconomical

HealthyFamilyCaloriesPlain

CrispRegularSugarFruit

ProcessedQualityTreatBoring

Nutritious

The 12 cereal brands were:

All BranCerola MuesliJust RightKellogg’s Corn Flakes

KompleteNutriGrainPurina MuesliRice Bubbles

Special KSustainVitabritWeetbix

a) Construct the correlation matrix for the ratings assigned to these 25 attributes by the
235 respondents. Looking at the correlations alone, can you identify some
attribute groups or clusters? (6 pts.)

b) Conduct a principal component analysis and construct a scree plot. How many
factors (m) do think you should retain? Explain. (4 pts.)

c) Conduct a factor analysis using Common Factor Analysis (diagonals = SMC) and
Principal Components as the Factoring method. If you are “happy” with your choice
of m then interpret & name the resulting factors. Include the rotated factor loading
matrix and include the Factor Loading Plots. (6 pts.)

d) Save the Rotated Components (i.e. Factor Scores) to the data spreadsheet. Change
the names of the Factor 1, Factor 2, etc. columns to the names you chose in part (c).

Next use Tables > Summary to compute the Mean Factor Scores for each Cereal.

Construct a scatterplot matrix of the mean factors and label the points by cereal
name. Discuss what these plots suggest about these cereals. (6 pts.)

You may need to Google some of these cereals as they are all brands from Australia to find out what

they are like.

2 –Mishel Uncertainty of Illness Survey (MUIS)

The data file Chronic Illness (MUIS).JMP contains data collected by Sonja Meiers et al. (2010). One of the surveys completed by subjects in the study was the Mishel Uncertainty of Illness Survey (MUIS). Due to the nature of some of the questions they needed to be recoded (as discussed in the context of McMaster’s survey in the lecture). The data file mentioned above contains all 31 items with some of them items recoded which is denoted by an “R” in the item number.

Use JMP to compute Cronbach’s for this sample of subjects completing the survey. Discuss. (5 pts.)